dvampire/autogenesis/figures-python

Name: figures-python
Author: dvampire

Claude Code and agent skill by dvampire/autogenesis

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By dvampire
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dvampire/autogenesis
autogenesis/skill/writing/figures-python/SKILL.md
Updated 26 Oct 2026
Version 1.0.0

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Checking that the SKILL.md file is still in the repository.

Use when creating data visualizations for papers - generates publication-quality plots with top-journal color schemes

Install

npx vskill@latest install dvampire/autogenesis/figures-python

SKILL.md

Python data figures

How to produce publication-quality data figures with Python.

Checklist

[ ] The conda environment (research) is activated
[ ] Figure type and data confirmed
[ ] The data manifest is recorded
[ ] Any mock/synthetic data is clearly labeled as planning data
[ ] A top-journal color scheme is used
[ ] Resolution set to 450 DPI
[ ] Both PNG and SVG exported
[ ] CJK font rendering checked (if the labels are Chinese)
[ ] Saved under figures/

1. Environment requirements

1.1 conda environment

Default environment name: research

Activate:

bash

conda activate research

Required libraries:

bash

pip install matplotlib seaborn numpy pandas

If the environment is not set up, invoke the environment-setup skill.

2. Figure standards

2.0 Data manifest and the mock-data boundary

Every data figure needs a data file and a data manifest first. Default paths:

text

figures/data-manifest.md
figures/data/<figure-name>.csv
figures/<section>/<figure-name>.py
figures/<section>/<figure-name>.png
figures/<section>/<figure-name>.svg

figures/data-manifest.md records at least:

Figure	Data file	Real/mock	Source	Script	Outputs

Mock or synthetic data may only be used for planning versions of a figure. The file name must start with mock_ or synthetic_, and the figure, table, or chapter draft must keep the placeholder [待真实实验替换] ("replace with real experiment"). Never describe mock data with "results show" or its Chinese equivalent 实验结果表明.

2.1 Resolution

Use	DPI	Notes
Journal submission	300–600	What most journals require
Top-tier submission	450+	Nature / Science and similar
Screen display	150	Slides, web

This skill defaults to 450 DPI.

2.2 Output formats

Export every figure in both formats:

PNG: raster, for web and slides
SVG: vector, for journal submission

2.3 Figure sizes

Type	Width (inches)	Use
Single column	3.5	Journal single column
Double column	7.0	Journal double column / full width
Slide figure	10.0	Presentations

3. Top-journal color schemes

3.1 Nature / Science style

python

NATURE_COLORS = ['#2E86AB', '#A23B72', '#F18F01', '#C73E1D', '#95C623']

3.2 Cell style

python

CELL_COLORS = ['#4E79A7', '#F28E2B', '#E15759', '#76B7B2', '#59A14F', '#EDC948']

3.3 Colorblind-safe

python

COLORBLIND_SAFE = ['#0077BB', '#33BBEE', '#009988', '#EE7733', '#CC3311', '#EE3377']

3.4 Color rules

Do NOT use matplotlib's default colors
Do NOT use pure red, pure blue, pure green, or other primaries
Keep a single figure to at most 5 colors
Make sure the palette is colorblind-safe

4. Code template

python

"""
Figure X: [figure title]
Paper section: [section this belongs to]
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
from pathlib import Path
# CJK font configuration (only needed for Chinese labels)
CHINESE_FONT = None
font_candidates = [
    '/System/Library/Fonts/STHeiti Light.ttc',
    '/System/Library/Fonts/PingFang.ttc',
]
for fp in font_candidates:
    if Path(fp).exists():
        CHINESE_FONT = fm.FontProperties(fname=fp)
        break
plt.rcParams['axes.unicode_minus'] = False
# Top-journal palette
COLORS = ['#4E79A7', '#F28E2B', '#E15759', '#76B7B2', '#59A14F']
def setup_plot_style():
    plt.rcParams.update({
        'font.size': 10,
        'axes.titlesize': 12,
        'axes.labelsize': 10,
        'axes.spines.top': False,
        'axes.spines.right': False,
        'axes.grid': True,
        'grid.alpha': 0.3,
        'legend.frameon': False,
        'savefig.dpi': 450,
        'savefig.bbox': 'tight',
    })
def main():
    setup_plot_style()
    fig, ax = plt.subplots(figsize=(7, 5))
    # === plotting code ===
    x = np.linspace(0, 10, 100)
    ax.plot(x, np.sin(x), color=COLORS[0], label='Model A')
    ax.plot(x, np.cos(x), color=COLORS[1], label='Model B')
    if CHINESE_FONT:
        ax.set_xlabel('时间 (s)', fontproperties=CHINESE_FONT)
        ax.set_ylabel('幅值', fontproperties=CHINESE_FONT)
    else:
        ax.set_xlabel('Time (s)')
        ax.set_ylabel('Amplitude')
    ax.legend()
    # === end plotting code ===
    # Save
    output_dir = Path(__file__).parent
    fig_name = Path(__file__).stem
    plt.savefig(output_dir / f'{fig_name}.png', dpi=450)
    plt.savefig(output_dir / f'{fig_name}.svg')
    plt.show()
if __name__ == '__main__':
    main()

5. Common plot types

Line chart

python

ax.plot(x, y, color=COLORS[0], linewidth=1.5, marker='o', markersize=4)

Bar chart

python

ax.bar(x_pos, values, color=COLORS[:len(values)], edgecolor='white')

Heatmap

python

im = ax.imshow(matrix, cmap='RdBu_r', aspect='auto')
plt.colorbar(im, ax=ax)

Box plot

python

bp = ax.boxplot(data_list, patch_artist=True)
for patch, color in zip(bp['boxes'], COLORS):
    patch.set_facecolor(color)

Scatter plot

python

ax.scatter(x, y, c=colors, s=sizes, alpha=0.6, cmap='viridis')

6. File management

Directory layout

figures/
├── chapter1/
│   ├── fig1_overview.py
│   ├── fig1_overview.png
│   └── fig1_overview.svg
├── chapter2/
└── chapter3/

Naming convention

File name format: fig{number}_{description}.py
Example: fig1_model_architecture.py

7. Quality check

Content

[ ] The data are correct
[ ] Axis labels are complete, including units
[ ] The legend is legible

Visuals

[ ] A top-journal palette is used
[ ] Resolution is 450 DPI
[ ] Font sizes are appropriate

Output files

[ ] PNG generated
[ ] SVG generated
[ ] File naming follows the convention

8. FAQ

Q1: Chinese characters render as boxes

python

from matplotlib.font_manager import FontProperties
font = FontProperties(fname='/System/Library/Fonts/STHeiti Light.ttc')
ax.set_xlabel('中文标签', fontproperties=font)

Q2: The image is blurry

python

plt.savefig('figure.png', dpi=450, bbox_inches='tight')

Q3: The legend covers the data

python

ax.legend(loc='upper left', bbox_to_anchor=(1.02, 1))

Security

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Model reviewNot runA model review is scheduled for the next rescan.

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Quality evaluation

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Source

SKILL.md in the repositoryCheckingautogenesis/skill/writing/figures-python/SKILL.md

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